# Lead Data & AI Engineer - Semantic Intelligence Source: https://realanalystjobs.com/job/lead-data-and-ai-engineer-semantic-intelligence-thomson-reuters-3928ad3d Lead Data & AI Engineer - Semantic Intelligence at Thomson Reuters, an on-site role based in Bengaluru, India. Posted on 5 November 2026, read from Thomson Reuters' own careers page (Workday), which RealAnalystJobs re-checks daily and removes the role when it closes. Updated 15 November 2026. ## Key facts - Company: [Thomson Reuters](https://realanalystjobs.com/companies/thomson-reuters) - Location: Bengaluru, India - Work mode: On-site - Posted: Nov 5, 2026 - Employment type: Full time - Source: Thomson Reuters' careers page (Workday), re-checked daily Skills: [SQL](https://realanalystjobs.com/skills/sql), [Python](https://realanalystjobs.com/skills/python), [Power BI](https://realanalystjobs.com/skills/power-bi), [dbt](https://realanalystjobs.com/skills/dbt), Snowflake, Airflow ## Visa sponsorship The posting does not say. Thomson Reuters had H-1B petitions approved for 41 workers in fiscal year 2026 (USCIS). That is history, not a promise for this role. [See the record](https://realanalystjobs.com/companies/thomson-reuters#h1b-record) ## About this role Job Description: We are looking for a Lead Data & AI Engineer - Semantic Intelligence to build trusted data and semantic foundations that turn enterprise AI into scalable business capability. You will combine data engineering, data-product thinking, dimensional and semantic modelling, application development, AI engineering, and practical data science. You will own delivery from raw data to AI-enabled decisions. This covers medallion architecture, reusable data products, semantic models, agents, and applications. You will partner early with business, product, Data & Analytics, and technology stakeholders to define outcomes, success measures, and data needs. You will turn ambiguous challenges into technical designs, prototypes, and production solutions, and you will stay involved through adoption and improvement. This is a hands-on technical leadership role with mentoring responsibilities. It has no direct people management. Key Responsibilities: • Deliver production-ready Data & AI solutions, staying hands-on in investigation, architecture, coding, modelling, testing, and implementation. • Build AI-ready data foundations and agentic solutions grounded in structured data, governed measures, semantic metadata, and enterprise knowledge. • Build production-ready Agentic AI, LLM, RAG, and tool/API orchestration patterns using enterprise engineering standards. • Build analytical and AI-enabled applications using Fabric application capabilities, Rayfin, or comparable frameworks, integrated with semantic models, APIs, agents, and enterprise security. • Build robust ELT/ETL pipelines using Snowflake, Microsoft Fabric, SQL, Python, dbt, Apache Airflow, or comparable technologies. • Implement bronze, silver, and gold medallion layers with lineage and auditability. Integrate lakehouse, warehouse, and semantic assets using batch, streaming, Direct Lake, or API patterns. • Deliver reusable, domain-aligned data products with clear ownership, data contracts, quality controls, documentation, and consumption interfaces. • Design dimensional models with explicit grain, conformed dimensions, facts, hierarchies, slowly changing dimensions, and bridge and relationship patterns. • Build and optimize Fabric and Power BI semantic models with governed measures, DAX, relationships, and metadata, so analytics, applications, and AI share consistent business context. • Explore data, frame hypotheses, engineer features, and apply segmentation, forecasting, classification, or anomaly detection where they support a business decision. • Evaluate analytical and machine-learning approaches against suitable baselines using statistical reasoning, validation, and business-relevant measures. Explain limitations and integrate useful results into data products or applications. • Lead end-to-end technical delivery: define scope, estimate and plan work, manage dependencies and risks, coordinate stakeholders, and own release readiness, adoption, and operational handover. • Mentor engineers through architecture, design, and code reviews, debugging, testing, and production-readiness practices. • Build data quality, observability, automated testing, lineage, security, privacy, responsible AI, version control, and CI/CD into every stage of delivery. • Turn successful solutions into reusable data products, semantic assets, agent capabilities, and engineering accelerators. • Start with the business decision or workflow, and co-shape outcomes, success measures, data needs, and adoption with stakeholders. • Move from discovery to prototype to production, validating assumptions with real enterprise data and early feedback. • Measure success by business value, trusted metrics, adoption, engineering quality, and reuse. Solve immediate needs while strengthening reusable enterprise foundations. Required Qualifications: • 8+ years of experience in data engineering, analytics engineering, AI engineering, or closely related technical roles. • Hands-on experience with Agentic AI, LLMs, agents, RAG, and tool/API integration or orchestration. • Experience building data-driven or AI-enabled applications using Fabric, Rayfin, or comparable frameworks. • Experience with Snowflake, Microsoft Fabric, or comparable analytical platforms, including lakehouse and warehouse patterns. • Strong SQL and Python skills, with experience delivering production-grade data solutions. • Experience delivering data products through bronze, silver, and gold layers, with quality controls, lineage, and clear consumption interfaces. • Strong dimensional modelling skills, including grain, facts, dimensions, star and snowflake schemas, slowly changing dimensions, and many-to-many patterns. • Proven enterprise semantic modelling experience, including governed business definitions and reusable measures. Proficiency in Power BI, DAX, and performance optimization, or equivalent technologies. • Working knowledge of exploratory analysis, statistical reasoning, feature engineering, and practical predictive methods such as forecasting, classification, or anomaly detection. • Ability to frame hypotheses, select baselines and evaluation measures, validate results, communicate uncertainty, and work with data scientists on modelling approaches. • Experience with orchestration, automated testing, version control, CI/CD, data quality, and operational support. • Ability to lead technical workstreams as a hands-on senior individual contributor and mentor engineers through design and code reviews. • Experience managing scope, estimates, dependencies, risks, releases, and operational handover, taking solutions from concept through adoption. • Clear communication with technical, business, and senior stakeholder audiences. Preferred Qualifications • Experience with dbt, Apache Airflow, Git/GitHub, and automated deployment frameworks. • Broader Power BI and Fabric experience across OneLake, Lakehouse, Warehouse, Notebooks, Direct Lake, and application capabilities. • Experience with AI solut Description from Thomson Reuters' public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you. ## How to apply Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/lead-data-and-ai-engineer-semantic-intelligence-thomson-reuters-3928ad3d) and use Apply now there to reach the employer's application. The listing is re-checked against Thomson Reuters' careers page daily and removed when it closes. ## Thomson Reuters 16 open analyst roles on Real Analyst Jobs. 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